Summary Background | Gallus gallus (chicken) is one of the most valuable model organisms for the study of the vertebrate embryo development. Such studies can be aided by pooling together OMICs data from public repositories, like GEO (Gene Expression Omnibus) and ArrayExpress, that currently contain more than 11.660 datasets from chicken, representing a wealth of data that can be explored to answer fundamental questions and generate new hypotheses. However, since these data come from different experiments, their meta-analysis requires proper normalization to deal with the technical biases and batch effects before making the data comparable for statistical analysis. Approach | An effective method for such normalization is the single-array pre-processing provided by the Frozen Robust Multiarray Analysis (fRMA) (McCall MN, et al. Biostatistics. 2010). This method uses “frozen” RMA vectors pre-computed from great amounts of available data for the same microarray platform, accounting for the aforementioned biases. However, such frozen vectors/parameters are available for multiple organisms (most notably, human, mouse, zebrafish, and fruit-fly among others), but not for chicken, hence preventing, or delaying, the proper meta-analysis of chicken transcriptomics datasets without prior computation of self-generated frozen parameters. Output | Here, we present the RData package FrozenChicken containing the chicken microarray frozen vectors that can be directly plugged-in to a chicken microarray analysis pipeline (using fRMA) without any other prior data gathering and processing. The package is freely available for the research community at: https://github.com/iduarte/FrozenChicken. Significance | This package will directly benefit the chicken research community by facilitating future meta-analysis studies using transcriptomics datasets from public repositories, hence directly contributing to the quality of the scientific research using the chick model organism.

This scientific work was funded by FCT, Portugal (grant PTDC/BEX-BID/5410/2014) and Research Center Grant UID/BIM/04773/2013 CBMR 1334. | * These authors contributed equally to this work (ordered alphabetically).